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Department 9.4

Metrology for sensor networks

Topic: Metrology for sensor networks

Sensor networks occur in industrial 4.0 environments as well as in autonomous systems that make decisions based on measurement data. The research work in this topic area focuses on linking the research work of the individual disciplines in order to address metrological questions for such sensor networks.

EPM 22DIT02 "Fundamental principles of sensor network metrology" (FunSNM)

Sensor networks are used in a large number of fields but are struggling with data quality of varying degrees, with unknown measurement uncertainty and lack of traceability to the SI limiting their applicability. To overcome these issues, this project will address the metrological aspects of sensor networks, covering the uncertainty propagation, data quality metrics and SI-traceability in generic sensor networks, as well as the assessment, infrastructure, and risk analysis of distributed sensor networks alongside software frameworks and semantics via automated application of developed methods. The applicability of the methods, tools, and concepts will be demonstrated in typical real-world sensor networks.

Project duration: 09/2023 - 08/2026

Contact: Opens local program for sending emailAnupam Vedurmudi

BMBF FAMOUS "AAS-based modeling for the analysis of changing cyber-physical systems"

For traditional industrial measurement and calibration methods, the metrological quality infrastructure is based on accredited calibration equipment and standardized evaluation methods in order to assign measured values a quantitative statement about their reliability. In the course of the digital transformation, the underlying methods must be fundamentally revised in order to be able to automatically determine the quality of measurement data in changing systems in the context of industry 4.0, since the quality and reliability of sensors can vary greatly due to different measurement capabilities and environmental influences.

In the project, individual sensors are linked to a digital twin that is able to communicate information about the measurement uncertainty. Sub-networks of sensors are combined in flexible mathematical models in order to enable machine-oriented data evaluation. With the help of organic computing methods, flexible and partially autonomous sub-networks are formed. Furthermore, a methodology is being developed to identify unsafe measuring points from aggregated measured values or characteristic values.

Contact person at PTB: Opens window for sending emailMaximilian Gruber

Website of the project: Opens external link in new windowhttps://famous-project.eu



Workflow Project FAMOUS